Through recent technical advances, multiple resources can be connected to provide a computing grid for processing computationally intensive applications. We build on an approach, termed sequential grid computing, that takes advantage of idle processing power by routing jobs that require lengthy processing through a sequence of processors. We present two models that solve the static and dynamic versions of the sequential grid scheduling problem for a single job. In the static and dynamic versions, the model maximizes a reward function tied to the probability of completion within service-level agreement parameters. In the dynamic version, the static model is modified to accommodate real-time deviations from the plan. We then extend the static model to accommodate multiple jobs. Extensive computational experiments highlight situations (a) where the models provide improvements over scheduling the job on a single processor and (b) where certain factors affect the quality of solutions obtained.

We analyze transaction data from a recently set up electronic exchange in India that trades in various grades of coffee. Such electronic exchanges have important implications for the supply chain of agricultural commodities and have generated significant interest in the trade press because of their potential to generate higher commodity prices for producers and reduced chances of unfair trading practices by middlemen. Policy makers view such exchanges as an important tool for extending the benefits of the information age to rural communities. We model the exchange from the perspective of a risk-averse buyer and we estimate the parameters of the model by comparing the prices in the electronic exchange with those of the same grade of coffee at physical auctions held weekly. We find that electronic auction prices are 4% higher and the difference is statistically significant. Further, we find that the price differential is higher for coffee grades that have higher price volatility and that are traded less frequently in the physical exchange. We also find that the price differential increases over time as buyers become more familiar with the benefits of the electronic trading format. We discuss the implications of our results for policy makers and providers of the electronic platform.

Topological design of communication networks has been well examined in the literature. However, most of these studies focus on nonhierarchical networks where all nodes are considered equivalent from a routing perspective. It is well recognized, however, that hierarchical topologies offer several advantages for remote office internetworking. In this article, we provide a mixed integer programming formulation for designing hierarchical topologies. We also provide an interesting solution strategy that incorporates valid inequalities within a dual ascent framework to obtain tight lower bounds and feasible solutions to the problem. Computational experiments with a Fortran implementation of the procedure are also reported.

A method is developed for integrating heuristic design knowledge with optimization models to create a tool for the topological design of computer communication networks. Design choices are based on suggestions from optimization models, as well as heuristic knowledge, which interact through a blackboard. A truth maintenance system (TMS) records justification for current design choices, as well as promising alternatives. A dependency-directed backtracking mechanism works with the TMS to choose other alternatives as warranted. This hybrid tool can consider a wider range of design requirements than is possible using one type of knowledge alone, is flexible in handling variations in these requirements, and has a modular structure which facilitates incremental refinement. Computational results on separate networks show it is effective in identifying good low-cost solutions.>